In this section, we explore several classification algorithms, including Logistic Regression, k-Nearest Neighbors (k-NN), and Decision Trees. Classification involves assigning labels to inputs based on their features and is widely used in applications such as spam detection, medical diagnosis, and image recognition. Logistic Regression, suitable for binary classification problems, estimates the probability of an instance belonging to a particular class and works well with linearly separable data. k-NN classifies new instances based on the majority class among its k-nearest neighbors and is effective for smaller datasets with irregular decision boundaries. Decision Trees, a non-parametric method, predict target values by learning decision rules from data features, capturing non-linear relationships. Each algorithm has its strengths and specific use cases, which we will explore in detail in the following sections through practical exercises. Stay tuned for an in-depth look at Logistic Regression and its applications in machine learning. Happy learning!